v2.0 — Kernel-Grade Features¶

MOL v2.0 introduces five systems that transform MOL from a scripting language into kernel-grade infrastructure for Neural Kernel and De-RAG / Sovereign Memory.

Overview¶

Feature	Purpose	Functions
Memory Safety	Rust-inspired ownership & borrowing	6 AST constructs
Vector Engine	Native SIMD-like vector operations	25 functions
Encryption	Homomorphic, symmetric, ZK proofs	15 functions
JIT Tracing	Self-optimizing hot-path detection	7 functions
Swarm Runtime	Distributed multi-node execution	12 functions

1. Memory Safety (Borrow Checker)¶

MOL enforces memory safety at the language level. No buffer overflows. No use-after-free. No data races.

Ownership¶

-- Only one owner at a time
let own buffer be [1, 2, 3, 4, 5]
show to_text(buffer)

Borrowing¶

-- Multiple immutable borrows allowed
let ref reader1 be borrow buffer
let ref reader2 be borrow buffer

-- Mutable borrow: only one at a time
let ref mut writer be borrow_mut buffer

Transfer & Release¶

-- Transfer ownership (original becomes invalid)
let own data be [10, 20, 30]
transfer data to new_owner

-- Explicit release
let own resource be "connection"
release resource

Lifetime Scopes¶

-- Resources auto-released at scope end
lifetime request_scope do
    let own temp be "scoped resource"
    show temp
end
-- temp is gone here

2. Vector Engine¶

First-class Vector type for De-RAG nanosecond retrieval. Backed by array.array for near-C performance.

Creating Vectors¶

let a be vec(1.0, 0.0, 0.0)
let zeros be vec_zeros(128)
let ones be vec_ones(128)
let random be vec_rand(128)
let embed be vec_from_text("hello world", 32)

Operations¶

let similarity be vec_cosine(a, b)
let distance be vec_distance(a, b)
let dot be vec_dot(a, b)
let normed be vec_normalize(a)
let sum be vec_add(a, b)
let scaled be vec_scale(a, 3.0)

ANN Search Index¶

let idx be vec_index("knowledge", 32)
vec_index_add(idx, vec_from_text("quantum computing", 32), "quantum")
vec_index_add(idx, vec_from_text("machine learning", 32), "ml")

let results be vec_index_search(idx, vec_from_text("deep learning", 32), 5)

ML Activations & Quantization¶

let activated be vec_relu(raw_vector)
let probs be vec_softmax(logits)
let compressed be vec_quantize(large_vector)  -- int8 compression

3. Integrated Encryption¶

Compute on encrypted data without decrypting. De-RAG's sovereign memory layer.

Homomorphic Encryption (Paillier)¶

let keys be crypto_keygen(512)
let enc_a be he_encrypt(42, keys)
let enc_b be he_encrypt(18, keys)

-- Math on ciphertext!
let enc_sum be he_add(enc_a, enc_b)
show he_decrypt(enc_sum, keys)    -- 60

let enc_scaled be he_mul_scalar(enc_a, 3)
show he_decrypt(enc_scaled, keys) -- 126

Symmetric Encryption¶

let key be sym_keygen(32)
let encrypted be sym_encrypt("secret data", key)
let decrypted be sym_decrypt(encrypted, key)

Zero-Knowledge Proofs¶

let commitment be zk_commit("my_secret")
let valid be zk_verify("my_secret", commitment["commitment"], commitment["blinding"])
let proof be zk_prove("my_secret")

4. JIT Tracing¶

MOL's interpreter automatically traces hot paths and optimizes them.

Automatic Optimization¶

Functions that execute 50+ times are profiled. After 100+ calls, type-specialized fast paths are engaged.

define fibonacci(n)
    let a be 0
    let b be 1
    let i be 0
    while i < n do
        let temp be b
        set b to a + b
        set a to temp
        set i to i + 1
    end
    return a
end

-- After many calls, JIT specializes for integer arithmetic
let i be 0
while i < 200 do
    fibonacci(20)
    set i to i + 1
end

Introspection¶

show jit_stats()        -- Runtime statistics
show jit_hot_paths()    -- List of optimized functions
show jit_profile("fibonacci")  -- Detailed profile
jit_toggle(false)       -- Disable JIT
jit_toggle(true)        -- Re-enable

5. Swarm Runtime¶

Treat a distributed network as a single CPU. Consistent hashing, MapReduce, fault tolerance.

Initialize & Manage¶

let cluster be swarm_init(5, 2)    -- 5 nodes, replication factor 2
show swarm_nodes(cluster)
show swarm_health(cluster)

Data Sharding¶

let data be ["item_1", "item_2", "item_3", "item_4"]
swarm_shard(data, cluster, "hash")     -- Consistent hash distribution
let gathered be swarm_gather(cluster)  -- Collect back

MapReduce¶

let mapped be swarm_map(cluster, fn(data) -> len(data))
let total be swarm_reduce(mapped, fn(acc, val) -> acc + val)

Communication & Scaling¶

swarm_broadcast(cluster, "sync_checkpoint")
swarm_scatter(cluster, task_list)

-- Dynamic scaling
swarm_add_node(cluster)
swarm_rebalance(cluster)
swarm_remove_node(cluster, "node-id")

Full Pipeline: Sovereign Memory¶

All five systems working together:

-- 1. Generate crypto keys
let keys be crypto_keygen(512)

-- 2. Embed documents as vectors
let idx be vec_index("knowledge", 32)
for doc in documents do
    vec_index_add(idx, vec_from_text(doc["text"], 32), doc["id"])
end

-- 3. Encrypt relevance scores
let scores be map(documents, fn(d) -> he_encrypt(42, keys))

-- 4. Distribute across swarm
let cluster be swarm_init(3, 2)
swarm_shard(doc_ids, cluster, "hash")

-- 5. Search (vector) + aggregate (encrypted) + distribute (swarm)
let results be vec_index_search(idx, vec_from_text(query, 32), 3)
let enc_total be reduce(scores, fn(acc, s) -> he_add(acc, s))
show he_decrypt(enc_total, keys)

-- JIT optimizes all of this automatically
show jit_stats()